KEY FEATURES
Architectural innovations that redefine AI infrastructure
Single Root Complex
Up to 128 accelerators as native peers on single PCIe fabric. True unified system architecture.
Unified Memory
18+ TB coherent memory in single address space. No fragmentation, no distributed overhead.
Deterministic Performance
Nanosecond-scale latency. Hardware-enforced coherence. Numerical reproducibility guaranteed.
Thermal Management
Proprietary bi-phase cooling maintains <30°C constant temperature. Zero thermal throttling.
Energy Efficiency
46% lower energy OPEX vs traditional clusters. €353,934 annual savings per 256-GPU deployment.
Sovereign Deployment
On-premise, air-gapped capable. Full data residency. Built for regulated environments.
INFRASTRUCTURE ECONOMICS
46% lower energy OPEX compared to NVIDIA DGX H200 256-GPU cluster
Same GPUs. Different Economics. GRIFO™ uses 4× fewer nodes, reducing server, CPU, and network overhead significantly. This translates to ~€1.77M savings over 5 years per 256-GPU deployment.
TECHNICAL SPECIFICATIONS
KEY DIFFERENTIATORS
Why GRIFO™ represents a different infrastructure class
Not a Server, Not a Cluster
GRIFO™ is a Single Root Complex Computational System. The OS sees it as one computer, not a collection of networked machines. No nodes, no network cards, no distributed communication stack.
Accelerator-Centric Architecture
Accelerators are the system, not peripherals. CPU handles bootstrap, supervision, and I/O only—never enters the critical path. Computation happens at hardware speed.
Physical and Mathematical Coherence
Unified memory space enables models that cannot be partitioned. Deterministic latency allows continuous-time computation. Physical design preserves mathematical integrity.
Computational Expansion
GRIFO™ doesn't just accelerate existing models—it makes entirely new classes of AI computationally feasible: Hamiltonian AI, massive coherent ensembles, continuous financial models, rigorous scientific AI.
WHAT BECOMES POSSIBLE
GRIFO™ makes physically computable entire classes of models that were previously non-realizable
Complex AI models that cannot be partitioned
Models intolerant to latency and approximation
Massive coherent ensembles requiring true synchronization
Hamiltonian and continuous-time AI systems
Continuous financial models with sub-microsecond requirements
Real-time autonomous systems with deterministic response
Rigorous scientific AI with numerical reproducibility
Future quantum-classical integration architectures